






import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


a						:			Any     	=					get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
       a						:			Union[str, Any]     	=					json.load(f)
@require_torch
class        a       (					unittest.TestCase  ):



      """simple docstring"""


      def       UpperCAmelCase ( self   :     int						, __lowercase   :     List[Any]   )				-> Tuple:
        return FSMTTokenizer.from_pretrained(__lowercase   )
      def       UpperCAmelCase ( self   :     Any						, __lowercase   :     Any   )				-> Any:
        __UpperCAmelCase				:      Any	 =    FSMTForConditionalGeneration.from_pretrained(__lowercase   ).to(__lowercase   )
        if torch_device == "cuda":
          model.half()
        return model




      @parameterized.expand(
          [
              ["""en-ru""", 26.0],
              ["""ru-en""", 22.0],
              ["""en-de""", 22.0],
              ["""de-en""", 29.0],
          ]   )
      @slow
      def       UpperCAmelCase ( self   :     List[str]						, __lowercase   :     List[Any]						, __lowercase   :     Optional[int]   )				-> List[str]:
        # note: this test is not testing the best performance since it only evals a small batch
        # but it should be enough to detect a regression in the output quality
        __UpperCAmelCase				:      Dict	 =    f"""facebook/wmt19-{pair}"""
        __UpperCAmelCase				:      Union[str, Any]	 =    self.get_tokenizer(__lowercase   )
        __UpperCAmelCase				:      Any	 =    self.get_model(__lowercase   )

        __UpperCAmelCase				:      Optional[Any]	 =    bleu_data[pair]["""src"""]
        __UpperCAmelCase				:      Optional[Any]	 =    bleu_data[pair]["""tgt"""]

        __UpperCAmelCase				:      Dict	 =    tokenizer(__lowercase						, return_tensors="""pt"""						, truncation=__lowercase						, padding="""longest"""   ).to(__lowercase   )
        __UpperCAmelCase				:      Optional[int]	 =    model.generate(
            input_ids=batch.input_ids						, num_beams=8						, )
        __UpperCAmelCase				:      Optional[Any]	 =    tokenizer.batch_decode(
            __lowercase						, skip_special_tokens=__lowercase						, clean_up_tokenization_spaces=__lowercase   )
        __UpperCAmelCase				:      Tuple	 =    calculate_bleu(__lowercase						, __lowercase   )
        print(__lowercase   )
        self.assertGreaterEqual(scores["""bleu"""]						, __lowercase   )








"""simple docstring"""




def        a__					(							snake_case__							)		->   list:

			if len(snake_case__							) <= 1:
						return [tuple(snake_case__							)]

			lowerCamelCase      =   []

			def generate(snake_case__			,					snake_case__							):
						lowerCamelCase      =   [0] * n
						res.append(tuple(snake_case__							)							)

						lowerCamelCase      =   0
						while i < n:
									if c[i] < i:
												if i % 2 == 0:
															lowerCamelCase ,  lowerCamelCase      =   arr[i], arr[0]
												else:
															lowerCamelCase ,  lowerCamelCase      =   arr[i], arr[c[i]]
												res.append(tuple(snake_case__							)							)
												c[i] += 1
												lowerCamelCase      =   0
									else:
												lowerCamelCase      =   0
												i += 1

			generate(len(snake_case__							)			,					snake_case__							)
			return res


if __name__ == "__main__":
					lowerCAmelCase				:     str					=				input("""Enter numbers separated by a comma:\n""").strip()
					lowerCAmelCase				:     List[Any]					=				[int(item) for item in user_input.split(""",""")]
					print(heaps(arr))



